NeurIPS 2026 Papers

As web agents close the gap with humans on benchmarks, it raises the question: *Do today's agents perform just as well on tomorrow's web?* We introduce TimeWarp, a benchmark that emulates the evolving web. TimeWarp consists of three web environments, each with six UI versions spanning UI design and frontend code from different eras of the internet. We pair TimeWarp with a set of complex, realistic tasks covering different forms of web navigation. Our experiments reveal vulnerabilities of web agents, especially visual ones, to changes and the limitations of behavior cloning (BC) on complex trajectories from a single version. To address this, we propose TimeTraj, a simple yet effective algorithm that uses plan distillation to collect trajectories across multiple versions. By training agents on teacher rollouts using our BC-variant, we achieve substantial performance gains: $20.4$ %$\rightarrow37.7$% for Qwen-3 4B and $0$ % $\rightarrow27.0$% for Llama-3.1 8B models. Our work helps study generalization across web designs and enables a new paradigm for collecting plans rather than trajectories to improve the robustness of web agents.

Path-integration-trained recurrent neural networks (RNNs) can reproduce grid-like and head-direction-like representations, but most existing models rely on low-dimensional velocity inputs and do not capture the hierarchical organisation of the brain's self-localisation circuit. We introduce a hierarchical recurrent neural network (HRNN) that separately processes angular velocity and linear speed to perform simultaneous angular and positional path integration. Trained on these tasks, the HRNN generalises to longer timescales than seen during training and develops neural response properties resembling all the key cell types: angular velocity cells, head direction cells, speed cells, conjunctive and pure grid cells. The learned circuit also recovers key motifs of the navigation system, including attractor dynamics and asymmetric connectivity patterns. Perturbation analyses reveal functional information flow across the hierarchy, consistent with experimental observations. Finally, introducing theta-paced mutual inhibition between two learned angular-velocity subpopulations enables the HRNN to reproduce the bidirectional theta sweeps observed in empirical data. These results establish hierarchical task-driven RNNs as a biologically interpretable framework for linking neural representations, circuit motifs, and population dynamics in spatial navigation, while enabling targeted in silico perturbations and the generation of experimentally testable predictions.

Deploying a language-guided navigation policy in a new building usually means watching it fail. Public-benchmark VLAs degrade scene by scene as layout, appearance, and language drift, and the standard fix of teleoperating fresh trajectories on site does not scale. We replace human demonstration with a single brief iPhone scan. Scan2Instruction compiles that scan into a complete training environment, automatically synthesizing executable navigation episodes with grounded instructions. PRPT-KS then adapts the policy through a closed-form Pareto utility read directly off the reconstructed scene, with no learned critic and no human labels. Across five real environments, the two designs together drive our RealNav to an 89% real-robot success rate, almost doubling the strongest VLA baseline at 48%, and still achieve 87% in unscanned regions of the same buildings. Our work turns demonstration-free scene adaptation for language-guided navigation from an aspiration into a working reality. All code and model will be released open-source upon acceptance.

Outdoor robot navigation in dynamic, unknown environments remains a formidable challenge, necessitating robust traversability assessment and collision-free planning. While traditional map-based approaches struggle with adaptability to novel scenes, existing map-free methods frequently incur high computational costs and depend heavily on LiDAR data. In response, we propose LiteNav, a vision-only for exteroceptive sensing and strictly map-free outdoor navigation system. Relying solely on a single off-the-shelf RGB camera and GPS for goal specification, LiteNav estimates traversability, encodes navigation goals, and generates candidate trajectories via a diffusion-based joint encoding model. These trajectories are then refined by a goal-oriented planner to ensure correctness, efficiency, and safety. Remarkably, LiteNav achieves performance comparable to LiDAR-equipped methods without ever constructing or referencing explicit maps. Deployed on a low-power NVIDIA Jetson Xavier NX, it operates in real time, consuming only one-fifth of the computational resources required by prior approaches. Experiments demonstrate that LiteNav achieves state-of-the-art results on the GND dataset and outperforms existing methods in real-world scenarios. In summary, LiteNav is a map-free outdoor navigation system that operates using monocular RGB perception and GPS goal cues on an embedded platform, demonstrating strong potential for scalable deployment.

Goal-conditioned visual navigation has been a long-standing testbed for embodied AI. We study a natural language-conditioned variant, language-conditioned visual navigation (LCVN), in which an embodied agent must follow a natural language instruction given only an initial egocentric observation. Without access to goal images, the agent must rely on language to shape its perception and continuous control. We introduce the LCVN Dataset, a benchmark of 39,016 trajectories and 117,048 human-verified instructions spanning diverse environments and instruction styles. Building on this benchmark, we study two complementary paradigms: (i) latent-imagination policy learning, in which a diffusion-based world model (LCVN-WM) imagines future observations and an actor-critic agent (LCVN-AC) learns its policy entirely within the imagined latent space; and (ii) unified autoregressive prediction, in which a single multimodal backbone (LCVN-Uni) jointly predicts actions and observations in one forward pass over a shared token sequence. Experiments show that two paradigms offer complementary strengths: latent imagination produces more temporally coherent rollouts, whereas unified prediction generalizes better to unseen environments. Targeted ablations further isolate the contributions of language guidance, conditioning signals, and instruction style, clarifying when language grounding versus dynamics modeling is the performance bottleneck. Together, these findings position LCVN as a concrete testbed for studying how language, imagination, and decision-making interact in embodied agents.

Zero-shot object goal navigation (ObjectNav) requires an embodied agent to locate a target object in an unseen environment without any task-specific training. Recent methods leverage large vision-language models (VLMs) to inject semantic priors, but still suffer from two structural issues: (i) semantic cues are treated uniformly across space, ignoring how the spatial extent and hierarchy of contextual entities modulate target likelihood; and (ii) semantic exploitation and geometric exploration are decoupled, producing brittle behavior whenever semantic cues are weak, conflicting, or absent. We address these issues by reformulating navigation as a unified value estimation problem over frontier candidates. Specifically, we propose InfoNav, which jointly models (1) a hierarchical semantic value map that instantiates a spatially-weighted total-probability decomposition of target likelihood through a multi-level spatial influence field; (2) a VLM-based estimator that approximates the intractable long-horizon information gain by grounding it in visual-semantic connectivity; and (3) a memory-augmented verification mechanism that replaces fixed confidence thresholds with a temporally adaptive schedule with deferred re-examination of uncertain detections. InfoNav establishes the highest zero-shot success rate on all three major ObjectNav benchmarks: 62.0% on HM3Dv1 (+0.6 over BeliefMapNav), 81.5% on HM3Dv2 (the only method above 80%), and 41.8% on MP3D (+4.5 over BeliefMapNav), while requiring more than 96% fewer multimodal-model calls than representative LLM-driven baselines. Extensive ablations confirm that the semantic and information-theoretic value fields are complementary, and that the unified formulation is what enables the agent to remain decisive under strong cues and exploratory under weak ones.

Vision-and-Language Navigation(VLN) requires agents to sustain task-directed spatial perception to focus on navigation-critical regions while translating linguistic intent into low-level actions. Prevalent hierarchical planning methods rely on a high-level policy to select waypoints or subgoals for low-level control, but they often fail to remain focused, causing inaccurate or redundant subgoals that directly mislead execution. Moreover, their separation between visual perception and action execution neglects fine-grained spatial reasoning in low-level movements and limits real-world deployability. To address these two issues, we propose FocusNav, a grounding-driven end-to-end monocular framework that learns task-directed spatial perception through action-aware future reconstruction. Concretely, FocusNav first boosts the agent’s visual attention on task-relevant regions with a Grounding-driven Visual Reconstruction module that reconstructs instruction-relevant targets and traversable areas via a semantic-guided latent diffusion process. Subsequently, to bridge the perception–execution gap, we incorporate physical action priors via Action-aware Visual Conditioning and enforce Intent-Effect Alignment between anticipated visual changes and observed action effects. These effectively enhance the agent’s task-directed spatial perception to direct attention to navigation-critical regions, and tighten the perception–execution loop. Experiments on R2R-CE and RxR-CE benchmarks show that FocusNav achieves substantially improved state-of-the-art performance with a simple monocular low-level policy, while providing faster inference than recent methods. Furthermore, real-world robot experiments validate our FocusNav’ s effectiveness, ease of deployment, and lightweight design.

We propose Drift-Resistant Navigation World Model, a generative model that mitigates both perceptual drift and geometric drift in conventional rollout-based navigation world models. Existing methods recursively feed generated content into subsequent steps, causing noise accumulation and degraded predictions, i.e., perceptual drift. Meanwhile, their predictions often deviate from the agent’s motion, resulting in geometry drift. We address both types of drift by redesigning world-model prediction as an anchor-guided rollout. Instead of rolling out every frame sequentially, we first predict sparse future anchors that serve as stable long-range targets, and then generate intermediate frames within each chunk conditioned on both past context and future anchors. Importantly, these sparse anchors also provide geometric constraints, supported by bidirectional epipolar geometry, to localize where corresponding content should appear in the intermediate frames. Experiments on four benchmarks demonstrate consistent improvements over strong baselines in long-horizon visual quality, geometric consistency, and multi-view coherence. These gains further translate into improved downstream planning performance under the same planners, highlighting the importance of drift-resistant, geometry-aware prediction for reliable navigation world models.